Large amounts of multimedia content, such as movies, music, and video, which are all readily accessible to users via the Internet can cause difficulties for users attempting to find particularly relevant content. Many users find searching through the vast quantities of content, much of the content comprising divergent types and categories, to be both cumbersome and time consuming. These situations have caused techniques for effectively recommending target information to become vital in the area of on-line content. Specifically, by efficiently recommending significantly smaller amounts of content (e.g., multimedia content), which may be considered relevant by a user, these mechanisms can considerably reduce that amount of information content e.g., that has to be searched, downloaded, or viewed by the user.
Current mechanisms for providing recommendations in popular content provider systems such as Netflix and Amazon are based on on-line purchasing histories and browsing histories of existing users. For example, Netflix, a provider of on-line movies, recommends a list of movies to be viewed by the current user based on the predetermined browsing history of other users. The recommendation is considered relevant, as it is based on existing users who also previously viewed the same movie as the current user. However, these known recommendation techniques generally rely on the responses and behaviors of various different users that may not be similar to that of the current user. Thus, recommendations provided using this aforementioned technique may not provide the content which is considered most relevant, or entertaining, to the user.